Every year millions of people use software for various purposes, e.g., keeping track of personal finances, managing projects, processing medical claims, managing corporate accounting and financial information, filing required documents with the government, tax planning, inventory management, business operation management (e.g., strategic planning, sales forecasting, distribution channel management, and other business-related activities), and the like. Many software applications are form-based in that the applications offer the ability to perform data entry, edit, and review via multiple forms associated with a common task. Specifically, software applications present multiple forms and collect inputs from a user through a user interface, such as a graphical user interface (GUI). In addition, form-based software applications may produce multiple output forms, or documents, based on the collected input data and certain algorithm embedded in the software application, commonly referred to as a generation engine or a calculation engine. The generation engine or the calculation engine may include functionalities to generate or calculate a result based on mathematical or logical operations.
Users of software applications (e.g., financial software, medical software, inventory control software, and the like) must be able to trust the quality of the data source behind the multiple forms presented by the software. Regardless of whether the data fields contain data inputted directly or derived from other data, a reviewer must have a means of determining the original data source, or sources, and assessing a level of trust, i.e., trust level, before the reviewer can determine the validity (e.g., correctness, exactness, accuracy, precision, trustworthiness, error or mistake-free, conformity to a standard or model, or according to other suitable measure of being valid) of the final output document. Maintaining a standard measure of quality is important when providing trusted data. Evaluating the quality of data and trusting the data source requires a system of quality control that often involves a multi-stage evaluation process to determine whether data from a source A, e.g., a temporary employee performing data entry, is any more or less valid than data automatically retrieved from another source B, e.g., a bank.
Defining trust level configurations and specifications is a knowledge intensive and time intensive process with a high maintenance cost since trust levels and data sources can change rapidly. A knowledgeable community is able to offer assistance when defining and maintaining trust level configurations by sharing community trust level offerings.